When it comes to the cellular industry in the U.S., there’s been a sea change over the last decade. And it’s definitely shifted the balance among the Big 4 telecom firms. AT&T and Verizon have long operated the two largest networks with the best overall coverage and the largest subscriber base. It appears that is […] Read More… iDrop News
AliveCor, the company that makes an FDA-approved EKG band for the Apple Watch called KardiaBand, teamed up with the Mayo Clinic for a new study that suggests an AliveCor EKG device paired with artificial intelligence technology can non-invasively detect high levels of potassium in the blood.
A second study conducted by the Cleveland Clinic also confirms the KardiaBand’s ability to accurately detect atrial fibrillation.
For the potassium study, AliveCor used more than 2 million EKGs from the Mayo Clinic from 1994 to 2017 paired with four million serum potassium values and data from an AliveCor smartphone EKG device to create an algorithm that can successfully detect hyperkalemia, aka high potassium, with a sensitivity range between 91 and 94 percent.
High potassium in the blood is a sign of several concerning health conditions, like congestive heart failure, chronic kidney disease, and diabetes, and it can also be detected due to the medications used to treat these conditions. According to AliveCor, hyperkalemia is associated with “significant mortality and arrhythmic risk,” but because it’s typically asymptomatic, it often goes undetected.
Currently, the only way to test for high potassium levels is through a blood test, which AliveCor is aiming to change with the new non-invasive monitoring functionality.
AliveCor says that the AI technology used in the study could be commercialized through the KardiaBand for Apple Watch to allow patients to better monitor their health. Vic Gundotra, AliveCor CEO, said that the company is “on the path to change the way hyperkalemia can be detected” using products like the Apple Watch.
For the Cleveland Clinic study, cardiologists aimed to determine whether KardiaBand for Apple Watch could differentiate between atrial fibrillation and a normal heart rhythm. The researchers discovered that the KardiaBand was able to successfully detect Afib at an accuracy level comparable to physicians interpreting the same EKGs. The Kardia algorithm was able to correctly interpret atrial fibrillation with 93 percent sensitivity and 94 percent specificity. Sensitivity increased to 99 percent with a physician review of the KardiaBand recordings.
KardiaBand, which has been available since late last year, is available for purchase from AliveCor or from Amazon.com for $199. Using the KardiaBand also requires a subscription to the AliveCor premium service, priced at $99 per year.
AliveCor premium paired with the KardiaBand offers SmartRhythm notifications, unlimited EKG readings, detection of atrial fibrillation or normal sinus rhythm, and unlimited cloud history and reporting of all EKGs.
The problem of so-called fake news is well known, yet we seem no closer to solving it. Social media is a major source of these falsehoods. Twitter, in particular, is responsible for much of their spread, so it doesn’t help that the platform’s executives recently dropped the ball, so to speak, on the whole issue.
Now, researchers from the Massachusetts Institute of Technology (MIT) are taking a look at the issue in one of the largest studies to date. Their findings suggest that humans – not bots – are largely to blame.
For their study, appearing in the March 2018 issue of the journal Science, the MIT team attempted to make sense of how and why fake news and misinformation spreads fast via Twitter. Specifically, they investigated how mechanisms in Twitter, coupled with peculiarities in human behavior on social media, make it easy for fake news to spread.
For their study, the team looked at a sample of some 126,000 bits of “news” tweeted by 3 million people more than 4.5 million times between 2006 and 2017.
“We define news as any story or claim with an assertion in it and a rumor as the social phenomena of a news story or claim spreading or diffusing through the Twitter network,” they wrote in the study. “That is, rumors are inherently social and involve the sharing of claims between people. News, on the other hand, is an assertion with claims, whether it is shared or not.”
Next, the researchers separated the news into two categories: false and true. To do this, they used six independent fact-checking organizations whose classifications showed a strong agreement.
Spreading Like Wildfire
After that, they examined how likely a piece of news was to create a “cascade” of retweets on the social networking platform.
Surprisingly, news categorized as false or fake was 70 percent more likely than true news to receive a retweet. “Political” fake news spread three times faster than other kinds, and the top 1 percent of retweeted fake news regularly diffused to at least 1,000 people and sometimes as many as 100,000.
True news, on the other hand, hardly ever reached more than 1,000 people.
The researchers also found a connection between the “novelty” of a bit of news and the likelihood that a Twitter user retweeted it.
In a study of 5,000 users, they looked at a random sample of tweets each user may have seen in the 60 days prior to retweeting a rumor. According to their analysis, false news was more novel than true news, and users were far more likely to retweet a tweet that was “measurably more novel.”
The emotional response a tweet generated also played a role in user engagement. Fake news generated replies showing fear, disgust, and surprise. True news inspired anticipation, sadness, joy, and trust. These emotions could play a role in a person’s decision to retweet a piece of news.
This spreading of misinformation isn’t due to bots, either – Vosoughi and his team used an algorithm to remove all the bots before conducting their analysis. When they factored the bots into the study, the researchers found that the bots didn’t distinguish between fake news and the truth.
“Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it,” they wrote in the study.
Just the Beginning
The MIT study isn’t the only fake news-related piece in the March 2018 issue of Science. It also includes a separate Policy Forum article co-authored by Filippo Menczer, a professor in the Indiana University School of Informatics, Computing, and Engineering.
“What we want to convey most is that fake news is a real problem, it’s a tough problem, and it’s a problem that requires serious research to solve,” said Menczer in a press release.
While the political repercussions of fake news are quite obvious, the phenomenon has affected various other discussions. As Menczer and his colleagues point out in their commentary, topics of concern to the public, such as vaccinations and nutrition, are susceptible to fake news, too.
“The challenge is there are so many vulnerabilities we don’t yet understand and so many different pieces that can break or be gamed or manipulated when it comes to fake news,” Menczer said in the press release. “It’s such a complex problem that it must be attacked from every angle.”
A good place to start that attack is with more studies like the one out of MIT.
A new study done by Consumer Intelligence Research Partners (CIRP) says that Android users have higher brand loyalty than iOS users, as reported by TechCrunch. The report says that not only has Android loyalty been rising since early 2016, but it’s currently the highest it’s ever been.
To measure current loyalty to each platform, the study looked at the percentage of US customers who stayed with their operating system after upgrading their phones in 2017. Ninety-one percent stayed with Android, while 86 percent stayed with iOS. Mike Levin, partner and co-founder of CIRP, tells TechCrunch that “With only two mobile operating systems at this point, it appears users now pick one, learn it, invest in apps and storage, and stick with it.”
Samsung’s new Galaxy S9 may not quite live up to the iPhone X when it comes to Samsung’s implementation of a Face ID-style system or its odd take on AR emoji. But that’s not going to matter much to Samsung device owners – not only because the S9 is a good smartphone overall, but because Android users just aren’t switching to iPhone anymore. In fact, Android users… Read More Mobile – TechCrunch
There's been a lot of discussion about fake news, how it spreads on social networks and how it impacts behaviors like political decisions. But there hasn't really been an in-depth look into how true and false information spreads on sites like Faceboo… Engadget RSS Feed
The US military's Project Maven is getting some help using AI to interpret drone footage from a not-entirely-unexpected source: Google. The company has confirmed a Gizmodo report that it's offering TensorFlow programming kits to the Defense Departm… Engadget RSS Feed
A recurring complaint about social media is that it fosters “echo chambers,” where people are encouraged to consume only content that reflects views they already hold. These bubbles of positive affirmation have been blamed for the spread of fake news, and are the subject of serious concern for those who use social media regularly. But there’s some good news: If the data in one recent study is anything to go by, the average social media users might not be opposed to outside views, especially if they trust the source. Social media agency The Data Face put together an experiment wherein…
“MIT = Mathematically Incompetent Theories (at least as it pertains to ride-sharing),” Uber CEO Dara Khosrowshahi tweeted.
The average Uber driver makes less than $ 4 an hour, at least according to a new paper published by MIT. In fact, the study, which coupled data from a survey of 1,100 drivers with vehicle cost information, found that 74 percent of drivers earned less than minimum wage in the state they worked in.
“Perhaps most surprisingly, the earnings figures suggested in the paper are less than half the hourly earnings numbers reported in the very survey the paper derives its data from,” Hall writes in a new post.
Even Uber CEO Dara Khosrowshahi sounded off on Twitter, saying MIT stood for “Mathematically Incompetent Theories.”
The Rideshare Guy survey — the underlying data used for the paper — found Uber drivers made an average of $ 15.68 an hour — but that’s before the costs of gas, maintenance and other expenses.
The MIT paper then incorporated the cost-per-mile for driving for Uber.
A brief on the study, which won’t be released in full for a few months, reads:
A Median driver generates $ 0.59 per mile of driving, and incurs costs of $ 0.30 per mile. 30% of drivers incur expenses exceeding their revenue, or lose money for every mile they drive. (Figure 1) On an hourly basis, the median profit is $ 3.37 per hour and 74% of drivers earn less than the minimum wage in the state where they operate.
Still, Uber claims the researchers’ methodology was flawed and further that drivers may not have understood the questions they were asked.
This is the crux of the company’s argument:
The Rideshare Guy survey asks a number of questions about how much drivers earn and how many hours they work per week. The most important are questions 11, 14, and 15.
Q11: “How many hours per week do you work on average? Combine all of the on-demand services that you work for.”
Q14: “How much money do you make in the average month? Combine the income from all your on-demand activities.”
Q15: “How much of your total monthly income comes from driving?”
The problem in this case is inconsistent logic on the part of the paper’s authors. Consider this: for question 14, the authors assume respondents are reporting income from *all* sources, not just on-demand work. As a result of this assumption, the authors discount the earnings from Q14 by the answer to Q15, “How much of your total monthly income comes from driving?”
For example: if a driver answered $ 1,000 to $ 2,000 to Q14, the authors would interpret that as $ 1,420.63² according to their methodology. If the respondent then answered “Around half” to Q15, the authors conclude this driver made $ 710.32 driving — half what they actually earned from driving with ridesharing platforms.
However, and perhaps just as important, the authors also assume that drivers understood Q11 perfectly well and that the hours reported only applied to on-demand work. As a result, they divide an incorrectly low earnings number by the correct number of hours.
We’ve reached out to the researchers and will update when we hear back.
If you are driving for Uber or Lyft, you aren’t going to make any money. In fact, you may actually be paying for the privilege of working at a ridesharing company. According to a study published by MIT, the median profit for drivers is an abysmal $ 3.37 an hour, and that’s before taxes. Ultimately, 74 percent of drivers earn less than minimum wage and, once vehicle expenses are taken into account, 30 percent actually lose money every mile they drive.
We’re just getting started.
The researcher also found that, in the U.S., an overwhelming majority of profits made while driving for Uber and Lyft aren’t taxed. For each mile driven, drivers incur about $ 0.30 in costs; however, they are able to claim a Standard Mileage Deduction of $ 0.54 per a mile on their taxes — a difference that amounts to billions of dollars in untaxed income.
So the drivers aren’t the only ones paying the price. We all are.
In an interview with The Guardian, an Uber spokesperson stated that the paper is little more than sensationalism, calling the methodology and findings “deeply flawed.” Yet, MIT isn’t exactly known for being a sensationalist publication.
In any case, the research paints a dark picture of the revolutionary ridesharing industry; however, it’s far from the first time the ride-hailing apps have faced censure. In September of last year, London decided not to renew Uber’s license to operate in the city, citing lacking corporate responsibility as one of the primary determinants.
Yet, the study is significant in that it highlights serious and untenable flaws in the gig economy model — a model that is fast creating a financial culture in which most people can’t survive. Mark Tluszcz, co-founder and CEO of Mangrove Capital Partners, succinctly summed the problem in an interview with TechCrunch: “We’re creating the next lost generation of people who simply don’t have enough money to live, and those companies are fundamentally enabling it under the premise that they’re offering a cheaper service to consumers.”
The problem stems from the fact that current policies weren’t written under a gig economy model, so platforms like Uber and Lyft are able to exploit loopholes in policy and avoid regulations that traditional companies must abide by. Studies like this highlight the reality that, for many, a secure financial future may depend on immediate updates to employment law.